Energy Efficient Target Coverage in Wireless Sensor Networks Using Adaptive Learning

Abstract

Over the past few years, innovation in the development of Wireless Sensor Networks (WSNs) has evolved rapidly. WSNs are being used in many application fields such as target coverage, battlefield surveillance, home security, health care supervision, and many more. However, power usage in WSNs remains a challenging issue due to the low capacity of batteries and the difficulty of replacing or charging them, especially in harsh environments. Therefore, this has led to the development of various architectures and algorithms to deal with optimizing the energy usage of WSNs. In particular, extending the lifetime of the WSN in the context of target coverage problems by resorting to intelligent scheduling has received a lot of research attention. In this paper, we propose a scheduling technique for WSN based on a novel concept within the theory of Learning Automata (LA) called pursuit LA. Each sensor node in the WSN is equipped with an LA so that it can autonomously select its proper state, i.e., either sleep or active with the aim to cover all targets with the lowest energy cost. Through comprehensive experimental testing, we verify the efficiency of our algorithm and its ability to yield a near-optimal solution. The results are promising, given the low computational footprint of the algorithm.

Using the Cuckoo Optimization Algorithm to Solve the Point Coverage Problem with Moving Targets in Wireless Sensor Networks

“…In [3], in order to increase the lifetime of the network, the nodes are divided into several cover sets so that the nodes in coverage are set to cover all the goals in the network. In this study, a method based on maximizing the number of cover sets is presented, and the nodes are categorized by linear programming and greedily in the cover sets.…”

“…Sensors are able to cover the target until their residual energy is greater than one threshold, and nodes whose residual energy is less than the threshold are excluded from the set of active nodes, and the remaining existing sensors must be able to cover the targets [8]. The selected cover nodes are sent to the main station after receiving the information and then provided to the enduser [3]. To increase the lifetime, monitoring energy consumption must also be minimized.…”

“…In general, sensors have four modes of data transmission, data reception, idle, and sleep [4,3]. Nodes in sleep mode have the lowest energy consumption; therefore, when they need to send and monitor the environment, they switch from sleep mode to active mode with a schedule.…”

“…Different solutions are provided to solve the problem of point coverage. In order to use energy efficiently in the matter of point coverage, the following solutions are presented [3].  Schedule node activities,  Power control by calibrating the transmission range of wireless nodes,  Data collection,  Reduce data transfer and prevent useless activity.…”